*Title: Econophysics and economics: the role of AI *Abstract: Econophysics and economics. Two scientific disciplines that have carved a long way into the subject of financial markets in the last 30 years, providing new theoretical models, methods and results. Nevertheless, despite sharing the same element of scientific investigation, they seem to proceed on strictly separated ways with an absolute lack of dialogue. By considering a crucial problem on a financial market (the early detection of “abnormal behaviors” such as financial crashes or bubbles), aim of my research is to bring in the best of both worlds: the trends and explanations via rational behaviours from economics and the apparent extreme behaviours from econophysics. The conceptual bridge is provided by the introduction of the concept of time asymmetry (i.e. irreversibility) as a fundamental component of economic behaviour. The asymmetry can be easily seen by direct inspection of most time series data for financial instruments in which it is clear that an equilibrium process is not generating the signal. We can model this disruption of equilibrium using concepts from Prigogine’s thermodynamics (the dissipative structures) and in so doing can explain the general dynamics of financial observables, rather than either the trend-like behaviour or the formation of bubbles and crashes. According to the dissipative structures conceptual paradigm, I have identified the news on a financial market (that is the complex system) as the crucial parameter explaining its changes of phase. The role for the AI inside this conceptual model is to detect possible way for demonstrating this role of the news, by measuring the level of entropy implied by the signal conveyed. The fundamental hypothesis is that a high level of entropy of the message inside the news allows for a stable market, whereas the opposite for the case of financial crashes. Therefore, aim of my research will be to develop possible algorithms to make a computer able to classify the financial news as information with low or high entropy, helping therefore the financial operator to identify the trend in the market. *Speaker: Luisella Balestra holds a degree in political science (Pavia University), PHD in economics (University of Milan), Diploma in financial economics (London University), and was a visiting research student in Harvard and LSE. She also will hold a Computer Science for Artificial Intelligence Professional Certificate (C language and Python) from Harvard (2020 - 2021) – edx.org. She has been an associate researcher at HSG S.Gallen University (FIM/ARC) and visiting researcher at the Center for the study of time, Faculty of Arts and Sciences with Prof.Dean Rickles, Sydney University, Australia. She’s also a teacher in economics for professional School in Ticino (Switzerland). *Date: Friday, December 18th, 2020, at 12:00 *Teams link: https://supsi.zoom.us/j/92900752754 ID meeting: 929 0075 2754 (no password needed)